Applying Deep Learning to MRI Image Analysis for Brain Tumor Classification

• Published in: International Conference on Bio-engineering for Smart Technologies (Online) pp. 1 - 5
• Main Authors: Alamiri, Deimah, Alfadhli, Rimah, Almutairi, Hajar, Alhabshi, Rahimah, Almutairi, Nour, Eleyan, Alaa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.05.2025
• Subjects: Accuracy; Artificial Intelligence; Brain modeling; Brain tumor Detection; Brain tumors; Convergence; Convolutional Neural Networks; Decision making; Deep learning; Explainable AI; Magnetic resonance imaging; MRI Classification; YOLO
• ISSN: 2831-4352
• DOI: 10.1109/BioSMART66413.2025.11046083

Automated and accurate brain tumor classification from MRI scans is a promising application of deep learning. This paper presents a YOLOv11-based deep learning model for detecting and classifying three tumor types: glioma, meningioma, and pituitary. Performance evaluation demonstrates the model's strong capability in tumor detection and classification, particularly with meningioma and pituitary tumors showing higher precision than glioma. Validation curves indicate steady reduction in loss functions across epochs, signifying effective learning and convergence. The model was trained and validated on a structured dataset, achieving a high accuracy performance of 98.9%. Deep learning-based tumor classification can facilitate early detection, assist radiologists, and improve clinical decision-making process.